Three-step estimation of latent Markov models with covariates

DOI10.1016/j.csda.2014.10.017OpenAlexW2088884405MaRDI QIDQ1623801

Giorgio E. Montanari, Francesco Bartolucci, Silvia Pandolfi

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1402.1033

zbMATH Keywords

EM algorithmlatent class modelnursing homespseudo likelihood methods

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Markov processes: estimation; hidden Markov models (62M05)

Related Items (8)

Causal inference for time-varying treatments in latent Markov models: an application to the effects of remittances on poverty dynamics ⋮ Estimating random effects in a finite Markov chain with absorbing states: Application to cognitive data ⋮ A two-step estimator for multilevel latent class analysis with covariates ⋮ Hierarchical Markov-switching models for multivariate integer-valued time-series ⋮ Evaluation of long-term health care services through a latent Markov model with covariates ⋮ Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach ⋮ A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error ⋮ Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models

Uses Software

• R
• msm

Cites Work

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• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
• Hidden Markov Models for Time Series
• A simple and fast alternative to the EM algorithm for incomplete categorical data and latent class models.

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